A bintree energy approach for colour image segmentation using adaptive channel selection |
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Authors: | Sheng-xian Tu Su Zhang Ya-zhu Chen Chang-yan Xiao Lei Zhang |
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Institution: | (1) Biomedical Instrument Institute, Shanghai Jiaotong University, Shanghai, 200240, China;(2) Department of Automation, Hunan University, Changsha, 410082, China;(3) Department of Computing, Hong Kong Polytechnic University, Hong Kong, China |
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Abstract: | A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features
are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the
best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method
based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with
other channels into new images, from which a new channel with better features is selected for the second round segmentation.
This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each
leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed
in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data
and tries to optimize the global segmentation result by choosing the “best” channel for segmentation at each level. The experiments
show that the method is effective in speed, accuracy and flexibility.
Foundation item: The National Basic Research Program (973) of China (No. 2003CB716103); The Key Lab of Image Processing & Intelligent control
of National Education Ministry (No. TKLJ0306) |
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Keywords: | active contour adaptive channel selection bintree energy segmentation color image |
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